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---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- ymoslem/IWSLT2023-GA-EN
- ymoslem/FLEURS-GA-EN
- ymoslem/BitesizeIrish-GA-EN
- ymoslem/SpokenWords-GA-EN-MTed
- ymoslem/Tatoeba-Speech-Irish
- ymoslem/Wikimedia-Speech-Irish
- ymoslem/EUbookshop-Speech-Irish
metrics:
- bleu
- wer
model-index:
- name: Whisper Medium GA-EN Speech Translation
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop
type: ymoslem/IWSLT2023-GA-EN
metrics:
- name: Bleu
type: bleu
value: 27.38
- name: Wer
type: wer
value: 72.17469608284557
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium GA-EN Speech Translation
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0491
- Bleu: 27.38
- Chrf: 51.97
- Wer: 72.1747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.02
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.6534 | 0.0138 | 100 | 2.2446 | 1.43 | 15.99 | 269.1130 |
| 2.4519 | 0.0276 | 200 | 2.1941 | 2.13 | 18.36 | 250.5178 |
| 2.2928 | 0.0414 | 300 | 2.0086 | 7.14 | 25.95 | 128.3656 |
| 2.233 | 0.0552 | 400 | 2.0239 | 5.61 | 24.25 | 134.0837 |
| 2.0406 | 0.0690 | 500 | 1.9215 | 5.64 | 25.65 | 183.8361 |
| 2.0273 | 0.0828 | 600 | 1.8556 | 13.41 | 30.96 | 83.7010 |
| 1.895 | 0.0966 | 700 | 1.8278 | 7.02 | 26.82 | 158.2170 |
| 1.9889 | 0.1103 | 800 | 1.7842 | 12.22 | 31.62 | 99.6398 |
| 1.8484 | 0.1241 | 900 | 1.7648 | 10.97 | 30.45 | 91.1751 |
| 1.7491 | 0.1379 | 1000 | 1.7498 | 10.0 | 29.42 | 109.0050 |
| 1.699 | 0.1517 | 1100 | 1.6662 | 12.53 | 34.87 | 109.9054 |
| 1.6959 | 0.1655 | 1200 | 1.6287 | 14.54 | 34.8 | 92.3008 |
| 1.6682 | 0.1793 | 1300 | 1.5800 | 13.26 | 33.5 | 103.0617 |
| 1.6625 | 0.1931 | 1400 | 1.6115 | 19.71 | 37.33 | 75.9118 |
| 1.5462 | 0.2069 | 1500 | 1.4993 | 18.3 | 39.49 | 93.7866 |
| 1.3834 | 0.2207 | 1600 | 1.4906 | 20.32 | 40.87 | 79.2436 |
| 1.39 | 0.2345 | 1700 | 1.4752 | 17.3 | 38.16 | 93.1562 |
| 1.5061 | 0.2483 | 1800 | 1.4004 | 20.11 | 39.69 | 81.0446 |
| 1.4125 | 0.2621 | 1900 | 1.3854 | 23.82 | 42.67 | 73.3904 |
| 1.3181 | 0.2759 | 2000 | 1.3979 | 20.57 | 40.87 | 78.8384 |
| 1.283 | 0.2897 | 2100 | 1.3446 | 17.97 | 40.47 | 88.8789 |
| 1.2061 | 0.3034 | 2200 | 1.3130 | 25.12 | 45.42 | 73.5254 |
| 1.2091 | 0.3172 | 2300 | 1.3274 | 22.12 | 43.56 | 79.8739 |
| 1.1264 | 0.3310 | 2400 | 1.2771 | 22.94 | 45.96 | 78.2080 |
| 1.0972 | 0.3448 | 2500 | 1.2858 | 24.38 | 46.04 | 75.4615 |
| 1.0822 | 0.3586 | 2600 | 1.2376 | 27.39 | 48.34 | 67.6722 |
| 1.0316 | 0.3724 | 2700 | 1.2461 | 28.0 | 47.61 | 68.5277 |
| 1.165 | 0.3862 | 2800 | 1.1869 | 26.05 | 48.13 | 71.6794 |
| 1.025 | 0.4 | 2900 | 1.1716 | 27.14 | 47.91 | 68.7528 |
| 0.8978 | 0.4138 | 3000 | 1.1628 | 28.34 | 49.15 | 65.6461 |
| 0.9146 | 0.4276 | 3100 | 1.1703 | 25.81 | 48.42 | 71.7244 |
| 0.9764 | 0.4414 | 3200 | 1.1526 | 29.63 | 51.22 | 67.3570 |
| 0.9455 | 0.4552 | 3300 | 1.1108 | 25.31 | 49.73 | 72.6249 |
| 0.9073 | 0.4690 | 3400 | 1.1085 | 27.7 | 50.85 | 72.7150 |
| 0.8596 | 0.4828 | 3500 | 1.0927 | 28.34 | 52.39 | 67.9424 |
| 0.8241 | 0.4966 | 3600 | 1.1026 | 29.95 | 51.37 | 65.2859 |
| 0.8436 | 0.5103 | 3700 | 1.0718 | 27.18 | 51.45 | 71.2292 |
| 0.8318 | 0.5241 | 3800 | 1.0678 | 30.71 | 53.35 | 64.3404 |
| 0.8262 | 0.5379 | 3900 | 1.0534 | 27.05 | 51.94 | 71.5894 |
| 0.8129 | 0.5517 | 4000 | 1.0491 | 27.38 | 51.97 | 72.1747 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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